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Question 1,337 of 1,755

Machine Learning Implementation and Operations →mediumMultiple ChoiceObjective-mapped

Quick Answer

The answer is to use SageMaker automatic model tuning with a schedule and update the endpoint using CreateEndpointConfig and UpdateEndpoint. This approach is correct because automatic model tuning can be configured as a recurring hyperparameter tuning job to retrain the model weekly, while the CreateEndpointConfig and UpdateEndpoint API calls enable a blue/green deployment strategy that shifts traffic to the new model variant without any downtime. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of SageMaker’s native mechanisms for automated retraining and zero-downtime endpoint updates, often contrasting them with less integrated options like Lambda-based retraining or batch transform. A common trap is choosing SageMaker Pipelines, which can orchestrate the workflow but still requires explicit endpoint update steps, or confusing batch transform (offline inference) with retraining. Memory tip: think “tune, config, update” — schedule the tuning job, create a new endpoint config, then update the endpoint to swap traffic seamlessly.

MLS-C01 Practice Question: Machine Learning Implementation and Operations

This MLS-C01 practice question tests your understanding of machine learning implementation and operations. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.

A machine learning team is deploying a model using Amazon SageMaker. They need to automatically retrain the model every week with new data and update the endpoint without downtime. Which approach should they use?

Question 1mediummultiple choice

Full question →

A
Use SageMaker Ground Truth to label new data and trigger retraining
Why wrong: Ground Truth is for labeling, not retraining scheduling.
B
Use SageMaker batch transform to periodically generate predictions and replace the model
Why wrong: Batch transform is for inference, not retraining.
C
Use AWS Lambda to trigger retraining on a schedule and deploy a new endpoint
Why wrong: Possible but not the best practice; SageMaker provides native scheduling.
D
Use SageMaker automatic model tuning with a schedule and update the endpoint using CreateEndpointConfig and UpdateEndpoint
This allows retraining and zero-downtime update.
E
Use SageMaker Pipelines to automate retraining and deploy a new endpoint with blue/green deployment
Why wrong: Pipelines can automate but blue/green requires manual endpoint update.

Full breakdown with real-world context →

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

Use SageMaker automatic model tuning with a schedule and update the endpoint using CreateEndpointConfig and UpdateEndpoint

Option C is correct because SageMaker automatic model tuning (hyperparameter tuning jobs) can be scheduled, and updating the endpoint with a new model can be done with CreateEndpointConfig and UpdateEndpoint for zero-downtime deployment. Option A (Lambda + retraining) is possible but not the most integrated. Option B (SageMaker Pipelines) can orchestrate retraining but updating endpoint may still be needed. Option D (batch transform) is for inference, not retraining. Option E (SageMaker Ground Truth) is for labeling.

Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Use SageMaker Ground Truth to label new data and trigger retraining
Why it's wrong here
Ground Truth is for labeling, not retraining scheduling.
✗
Use SageMaker batch transform to periodically generate predictions and replace the model
Why it's wrong here
Batch transform is for inference, not retraining.
✗
Use AWS Lambda to trigger retraining on a schedule and deploy a new endpoint
Why it's wrong here
Possible but not the best practice; SageMaker provides native scheduling.
✓
Use SageMaker automatic model tuning with a schedule and update the endpoint using CreateEndpointConfig and UpdateEndpoint
Why this is correct
This allows retraining and zero-downtime update.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use SageMaker Pipelines to automate retraining and deploy a new endpoint with blue/green deployment
Why it's wrong here
Pipelines can automate but blue/green requires manual endpoint update.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.

Detailed technical explanation

How to think about this question

This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.

KKey Concepts to Remember

Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
Use explanations to understand the rule behind the answer.

TExam Day Tips

Underline the problem statement mentally.
Watch for words such as best, first, most likely and least administrative effort.
Review why wrong options are wrong, not only why the correct option is correct.

Key takeaway

Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Real-world example

How this comes up in practice

A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.

What to study next

Got this wrong? Here's your next step.

Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.

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FAQ

Questions learners often ask

What does this MLS-C01 question test?

Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Use SageMaker automatic model tuning with a schedule and update the endpoint using CreateEndpointConfig and UpdateEndpoint — Option C is correct because SageMaker automatic model tuning (hyperparameter tuning jobs) can be scheduled, and updating the endpoint with a new model can be done with CreateEndpointConfig and UpdateEndpoint for zero-downtime deployment. Option A (Lambda + retraining) is possible but not the most integrated. Option B (SageMaker Pipelines) can orchestrate retraining but updating endpoint may still be needed. Option D (batch transform) is for inference, not retraining. Option E (SageMaker Ground Truth) is for labeling.

What should I do if I get this MLS-C01 question wrong?

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.